Implementing Applied Customer Engagement+ across all channels and touchpoints takes time, but real progress can be achieved in the near term. Start with a strategy to help in prioritizing impact, feasibility and desirability—and use that strategy to identify both quick wins and longer-term strategic priorities.
Whether implementing a new intelligent agent or a full customer engagement platform, don’t try to cover every possible interaction. Stand up an agent that’s trained to address a discrete number of customer needs on a select set of digital properties. Prove the concept in terms of the utility—enhancing the AI’s training while also building the organization’s own capacity for successfully managing such initiatives.
For many organizations, AI-powered customer engagement will progress through three major stages:
- Inform – providing customers and/or agents with relevant information. This could be accomplished with existing chatbots.
- Intervene – proactively guiding users through a specific process or transaction using tools like virtual assistants.
- Interpret – assessing and proactively addressing higher-order needs, whether the customer is interacting directly with the AI or the AI is in the background helping the customer service rep. For AI to reach this level, an organization would need higher-level training on customer intents and conversational flows.
Applied customer engagement+ at work
How would Applied Customer Engagement+ affect interactions? We map the potential impacts in the shift from anticipating to assisting to advising with the help of AI.
A look at how Applied Customer Engagement+ would affect interactions
AI -> Customer
Customer <-> AI
Customer <-> Agent <-> AI
||Proactive Informational Initiation
||Customer Knowledge Prompting
||Agent Knowledge Prompting
||AI proactively pushes information to customer
||AI help requested by customer to gather information
||AI pushes or agent pulls information during an interaction with a customer
||Proactive Service Resolution
||Customer Transaction Assistance
||Agent Service Support
||AI proactively helps a customer start or finish a task(service resolution)
||AI help requested by customer to complete an action (transactional chatbot)
||AI provides help in completing an action to agent during an interaction with a customer
||Customer Support Escalation
||AI recognizes patterns in customer activity and reaches out about potential issues or opportunities
||AI escalates to agent based on sentiment analysis, pattern recognition, and contextual data from a user profile within an existing conversation
||AI advises on how to interact with customer, including recommending phrases to use
Preparing for a "human+" workplace
Technology in the workplace affects workers on every level. Today’s employees can leverage the latest technologies to reinvent existing roles and find new, innovative ways to adapt and thrive in the post-digital era. And while much of the conversation about AI has been about the threat of eliminating jobs, in reality, AI empowers workers to become what Accenture calls “human+.”
When it comes to customer service, federal organizations can use AI to make everyone “superhuman”:
- Customers can self-serve by interacting with a channel-specific chatbot or voicebot.
- Customer service representatives can engage with AI as a virtual co-worker. Agent-assist or augmentation AI can listen to real-time conversations and suggest relevant resources to offer the customer; other tools can monitor the speed and clarity of the agent’s speech and advise when to slow down or when to adjust based on tone of the customer’s voice.
- Back-office employees—including those at service centers responsible for converting physical files to digital content—can get help from RPA tools that automate those repetitive processes. That frees their time and talent to focus on activities that deliver more value.
- Customer service leaders benefit from the power of machine learning and advanced analytics to help make predictions and to run what-if scenarios for how to optimize limited resources to better serve customers while gaining operational efficiencies. For example, natural language processing can be used for sentiment analysis to gauge satisfaction, with automated alerts used to flag emerging issues.
Think big. Start small. Scale fast.
AI-enabled customer service and customer experience tools are evolving quickly, offering commercial and government organizations unprecedented opportunities to reshape engagement.
While the opportunities are vast, the first steps can be straightforward. Start with a holistic strategy and business case. Focus on areas that will deliver the greatest value and/or be the most expeditious to automate. Then use momentum from those implementations to extend and expand quickly—transforming how the organization engages with customers and delivers mission outcomes.